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Duration 7 hours
Course Outline
Best Practices and Tools
Common Pitfalls and Mitigation Strategies
Introduction to Prompt Engineering
Prompt Refinement and Iterative Design
Prompting for Test Automation and SQL Generation
Summary and Next Steps
Using Prompts for Code Explanation and Debugging
Writing Prompts for Code Generation
- Preventing hallucinated code or security vulnerabilities.
- Managing incomplete or ambiguous inputs effectively.
- Establishing safe fallback prompts and implementing guardrails.
- Deriving test cases from requirements or existing code.
- Translating natural language into structured SQL queries.
- Formatting outputs for seamless integration into test suites.
- Clarifying legacy or unfamiliar code segments.
- Requesting logic walkthroughs or edge case analysis via prompts.
- Identifying and explaining bugs or performance inefficiencies.
- Generating code from plain-language descriptions.
- Controlling output formats and specifying programming languages.
- Handling complex logic or multiple function interactions.
- Enhancing outcomes through prompt chaining and feedback loops.
- Applying error recovery and prompt tuning strategies.
- Analyzing case studies on refinement for technical tasks.
- Utilizing prompt libraries and reusable patterns.
- Implementing prompt templates in VS Code or API-based workflows.
- Assessing prompt quality and performance in production environments.
- Grasping the fundamentals of prompts, context, tokens, and models.
- Differentiating prompt types: zero-shot, one-shot, and few-shot.
- Applying system vs. user instructions across different APIs.
Requirements
Audience
- Developers leveraging LLMs for code generation or analysis.
- Technical leads exploring the integration of AI tools into their workflows.
- Software professionals experimenting with LLM integrations.
- Practical experience in software development or scripting.
- Proficiency with common programming languages (e.g., Python, JavaScript, SQL).
- Foundational understanding of large language models and AI tools such as ChatGPT, Claude, or Copilot.
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
